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Automatic identification of otological drilling faults: an intelligent recognition algorithm
Author(s) -
Cao Tianyang,
Li Xisheng,
Gao Zhiqiang,
Feng Guodong,
Shen Peng
Publication year - 2010
Publication title -
the international journal of medical robotics and computer assisted surgery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.556
H-Index - 53
eISSN - 1478-596X
pISSN - 1478-5951
DOI - 10.1002/rcs.312
Subject(s) - computer science , drilling , identification (biology) , process (computing) , drill , interference (communication) , algorithm , filter (signal processing) , base (topology) , machining , artificial intelligence , pattern recognition (psychology) , data mining , computer vision , engineering , mathematics , mechanical engineering , mathematical analysis , channel (broadcasting) , botany , biology , operating system , computer network
Background This article presents an intelligent recognition algorithm that can recognize milling states of the otological drill by fusing multi‐sensor information. Methods An otological drill was modified by the addition of sensors. The algorithm was designed according to features of the milling process and is composed of a characteristic curve, an adaptive filter and a rule base. The characteristic curve can weaken the impact of the unstable normal milling process and reserve the features of drilling faults. The adaptive filter is capable of suppressing interference in the characteristic curve by fusing multi‐sensor information. The rule base can identify drilling faults through the filtering result data. Results The experiments were repeated on fresh porcine scapulas, including normal milling and two drilling faults. The algorithm has high rates of identification. Conclusions This study shows that the intelligent recognition algorithm can identify drilling faults under interference conditions. Copyright © 2010 John Wiley & Sons, Ltd.